LEADER 04285nam 22006735 450 001 9910427678803321 005 20251225185117.0 010 $a3-030-61598-7 024 7 $a10.1007/978-3-030-61598-7 035 $a(CKB)4100000011515579 035 $a(DE-He213)978-3-030-61598-7 035 $a(MiAaPQ)EBC6381261 035 $a(PPN)254615392 035 $a(EXLCZ)994100000011515579 100 $a20201019d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Medical Image Reconstruction $eThird International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings /$fedited by Farah Deeba, Patricia Johnson, Tobias Würfl, Jong Chul Ye 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (VIII, 163 p. 76 illus., 48 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12450 300 $aIncludes index. 311 08$a3-030-61597-9 327 $aDeep Learning for Magnetic Resonance Imaging -- 3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI -- Deep Parallel MRI Reconstruction Network Without Coil Sensitivities -- Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data -- Deep Recurrent Partial Fourier Reconstruction in Diffusion MRI -- Model-based Learning for Quantitative Susceptibility Mapping -- Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks -- Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping -- Data-Consistency in Latent Space and Online Update Strategy to Guide GAN for Fast MRI Reconstruction -- Extending LOUPE for K-space Under-sampling Pattern Optimization in Multi-coil MRI -- AutoSyncoder: An Adversarial AutoEncoder Framework for Multimodal MRI Synthesis -- Deep Learning for General Image Reconstruction -- A deep prior approach to magnetic particle imaging -- End-To-End Convolutional NeuralNetwork for 3D Reconstruction of Knee Bones From Bi-Planar X-Ray Images -- Cellular/Vascular Reconstruction using a Deep CNN for Semantic Image Preprocessing and Explicit Segmentation -- Improving PET-CT Image Segmentation via Deep Multi-Modality Data Augmentation -- Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning. 330 $aThis book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually. The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12450 606 $aArtificial intelligence 606 $aComputer vision 606 $aSocial sciences$xData processing 606 $aEducation$xData processing 606 $aBioinformatics 606 $aArtificial Intelligence 606 $aComputer Vision 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputers and Education 606 $aComputational and Systems Biology 615 0$aArtificial intelligence. 615 0$aComputer vision. 615 0$aSocial sciences$xData processing. 615 0$aEducation$xData processing. 615 0$aBioinformatics. 615 14$aArtificial Intelligence. 615 24$aComputer Vision. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputers and Education. 615 24$aComputational and Systems Biology. 676 $a616.07540285 702 $aDeeba$b Farah 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910427678803321 996 $aMachine Learning for Medical Image Reconstruction$91912511 997 $aUNINA